Blood Test Foresees Decline into Alzheimer’s Disease

A blood-based biomarker could predict a person’s risk of developing Alzheimer’s disease years before any symptoms arise, research suggests.

Plasma levels of phosphorylated tau 217 (p‑tau217) may one day help identify at-risk individuals before overt signs of dementia, enabling the pre-emptive use of disease-modifying therapies.

Higher plasma p‑tau217 levels were associated with a greater risk of progressing to cognitive impairment in previously unaffected older adults, and they also predicted faster levels of decline.

The research findings appear in JAMA and were simultaneously presented this week at the annual Alzheimer’s Association International Conference in London.

“In this longitudinal study of several selected cohorts, plasma p-tau217 provided long-term prognostic information for individuals who were cognitively unimpaired at baseline, laying the groundwork for possible future development of individualized risk prediction scores,” proposed Rachel Buckley, PhD, from Mass General Brigham, and co-workers in their published work.

“By providing absolute risk estimates of progression to cognitive impairment, this article moves the field closer to presymptomatic risk stratification with p-tau217, supporting trial design.”

The large, pooled multicohort study included 2684 cognitively unimpaired older adults from six longitudinal studies, who were followed for a median of 5.4 years. Their median age was just short of 70 years, and 63% were women.

Results showed that higher baseline p-tau217 was significantly associated with an increased risk of progression to cognitive impairment during up to 13.5 years of follow up, with a hazard ratio of 1.38 per standard deviation (SD) increase.

This remained significant after accounting for age, sex, education, apolipoprotein E ε4 status, cohort, as well as amyloid positron emission tomography—known to accurately detect Alzheimer’s disease brain pathology.

The researchers report that the absolute risk of cognitive decline at five years was “meaningfully elevated” in the group with very high p-tau217 levels (≥2.5 SD) at 38%, versus just 12% in group with low levels.

Estimated 10-years risks were substantially higher at between 40% and 78% for the low and high p-tau217 groups, respectively. However, just 139 participants—or one in every 20—were followed up for at least a decade and the researchers say these risk estimates should be treated with caution.

Elevated p-tau217 was also associated with faster decline on the harmonized latent Preclinical Alzheimer Cognitive Composite assessment tool.

In an editorial accompanying the published study, Suzanne Schindler, PhD, from Washington University in St Louis school of medicine, and David Wolk, MD, from the University of Pennsylvania, note that cognitive impairment likely reflected multiple etiologies, not just Alzheimer’s disease.

“Indeed, even the low p-tau217 group, in which Alzheimer disease pathology was minimal or absent, still had a 12% risk of progression to cognitive impairment at five years, suggesting the importance of other drivers of cognitive decline in this population and the likelihood that in the higher p-tau217 groups, some proportion of individuals who declined may have primarily been driven by other processes,” they pointed out.

“Notably, discordance between plasma p-tau217 and amyloid PET (especially at intermediate or low amyloid levels) highlights that biomarkers beyond p-tau217 could further improve risk prediction.”

Nonetheless, overall they summarized: “The study by Buckley et al. represents a significant advance. It demonstrates that plasma p-tau217 can provide a time-specific absolute risk estimate for development of cognitive impairment.”

The post Blood Test Foresees Decline into Alzheimer’s Disease appeared first on Inside Precision Medicine.

BFRBs vs. OCD: Similarities and Differences

This blog was originally posted by the TLC Foundation for BFRBs

Body-focused repetitive behaviors (BFRBs) and obsessive-compulsive disorder (OCD) are two distinct mental health conditions that share some similarities but also have significant differences. BFRBs involve repetitive, self-grooming behaviors that can cause physical damage, such as hair pulling or skin picking. On the other hand, OCD is a condition characterized by intrusive thoughts (obsessions) and repetitive behaviors (compulsions) performed to alleviate anxiety. 

While both conditions involve repetitive behaviors and can impact daily life, their underlying mechanisms, triggers, and treatment approaches differ. This article explores the key similarities and differences between BFRBs and OCD to better understand these complex conditions.

Similarities Between BFRBs and OCD

Most professionals view BFRBs and OCD as similar conditions due to the similarity in symptoms, such as compulsivity and repetitive behaviors. These two conditions share several similar systems and are usually a reaction to triggering factors such as stress and anxiety. Below are some of their similarities.

Repetitive Behaviors

Individuals dealing with BFRBs often engage in various repetitive behaviors such as hair pulling, lip biting, or skin picking. These actions are usually challenging to control and are frequently triggered by stress or anxiety. One may indulge in the habit subconsciously to find instant relief from the trigger. 

Individuals with OCD often experience intrusive thoughts that result in repetitive behaviors known as compulsions. Some common compulsions include washing hands and repetitively checking or counting to alleviate the stress caused by obsessive thoughts. In both conditions, the repetitive behaviors are often exacerbated by stress and anxiety, and individuals may adapt these behaviors as a coping mechanism.

Impulse Control

Closely related to repetitive behaviors is the concept of impulse control. Both BFRBs and OCD involve challenges in this area, albeit in different ways. Individuals with BFRBs and OCD may find it hard to control the urge to perform repetitive behaviors. This is because these repetitive behaviors often relieve tension. Despite knowing the consequences of these behaviors, the desire to indulge in them is usually irresistible. 

For example, individuals with BFRBs understand that hair pulling may affect their appearance, but they cannot refrain from doing it. OCD occurs as a result of intrusive thoughts whereby one believes that if they do not perform a specific action, the stressor won’t go away. These intrusive thoughts often cause anxiety, which can be eased by engaging in the said repetitive behavior.

Onset and Course

Having examined the behavioral aspects, let’s now consider how these conditions develop over time. The onset of these two conditions shares several similarities regarding age, triggers, and psychological mechanisms. 

The onset of both conditions is usually during childhood or adolescence and often coincides with various developmental changes and stressors. For individuals with BFRBs, the repetitive behaviors alleviate stress and anxiety instantly. At the same time, for those with OCD, performing the compulsions temporarily relieves them from the stress caused by their intrusive thoughts. The cognitive patterns involve repetitive actions, intrusive thoughts, and a lack of impulse control. In BFRBs, the urge to engage in these repetitive behaviors can be intrusive and persistent, while in OCD, one’s obsessions create a sense of urgency, which leads to the adoption of compulsive actions.

Neurobiological Factors

To fully understand the similarities between BFRBs and OCD, we must delve deeper into their biological underpinnings. Both conditions have a genetic origin and are associated with neurobiological factors. Neurobiological studies indicate that the impulse control and emotional regulation difficulties for people with BFRBs and OCD are often caused by abnormalities in brain regions that are responsible for impulse control and habit formation. Therefore, the underlying brain mechanism may result in the onset and development of both conditions. It is not uncommon for individuals to have both BFRBs and OCD or for both conditions to coincide with other mental health conditions, usually depression and anxiety. The overlap is generally because they typically share common underlying factors that play a part in their severity and development.

Differences Between OCD and BFRBs

While BFRBs and OCD share several commonalities, it’s equally important to understand their distinct characteristics, from the symptoms to the underlying mechanisms. Let’s explore the key differences that distinguish these two conditions.

Nature of the Behavior

First and foremost, let’s examine how the behaviors associated with each condition differ in their fundamental nature. Individuals dealing with these two conditions adopt diverse behaviors as coping mechanisms for their triggers. In BFRBs, the behaviors adopted, such as trichotillomania (hair-pulling) or cheek-biting, usually result in physical harm. However, regardless of the consequences, one always feels relieved when picking their skin or pulling their hair. 

OCD, on the other hand, involves a wide range of compulsions, from washing to organizing, checking, and counting. Compulsive behaviors are performed due to intrusive thoughts that make one think that if they fail to indulge in a specific behavior, they might get hurt, or there might be other negative consequences.

Presence of Obsessions

Another crucial distinction lies in the cognitive processes behind these behaviors. Generally, BFRBs do not involve obsessive thoughts. The primary focus on BFRBs is usually more on the physical behavior and not the fear of specific consequences. 

However, the major characteristic of OCD is intrusive thoughts, which increase the urge to indulge in particular behaviors for relief. The thoughts are usually persistent with unwanted images that result in distress. 

People with BFRBs DO NOT report that if they do not pick on their skin, something terrible will happen. Instead, they report that picking or pulling their hair helps relieve them from intense and negative emotions. These behaviors, therefore, serve a self-regulatory function, unlike in OCD, where the repetitive behavior calms them from their intrusive thoughts.

Triggers

The nature of triggers for each condition is closely related to the presence or absence of obsessions. The primary trigger in BFRBs is stress and anxiety, but for OCD, the main trigger is intrusive thoughts, which then result in anxiety. OCD and BFRBs triggers differ in several ways, often resulting in different outcomes. OCD triggers often result in one taking measures to prevent harm, while for BFRBs, one uses the adopted behaviors to regulate and manage intense emotions. The nature of thoughts is an essential distinguishing factor, seeing as OCD involves intrusive and obsessive thoughts that trigger specific behaviors adopted to prevent harm. The purpose of compulsions in OCD is to reduce the anxiety caused by the obsessive thoughts, while in BFRBs, the behaviors are for emotional relief.

Awareness

Beyond triggers, the level of conscious awareness also differentiates these two conditions. Those dealing with BFRBs usually find themselves biting their nails or even pulling their hair subconsciously. Individuals with OCD are generally aware of their intrusive thoughts and are compelled to adopt specific behaviors as a response to these thoughts. Individuals with OCD are often aware of their compulsions and understand when they are being irrational, but they are unable to control themselves. Compared to people with OCD, those with BFRBs often find their behaviors more rewarding than distressing.

Treatment

Finally, while both conditions may benefit from cognitive behavioral therapy, the specific approach to treatment varies significantly. For individuals with BFRBs, the focus is on behavior modification and awareness, achieved through habit reversal training. For OCD, the emphasis is often placed on exposure to anxiety-provoking thoughts to help an individual tolerate anxiety, which prevents compulsive behavior.

Bottom Line 

While BFRBs and OCD can coexist, they are distinct disorders with unique manifestations despite sharing some similarities. The key distinctions between these conditions are evident in their underlying mechanisms and treatment approaches.

Both involve compulsive behaviors, but their purposes differ. BFRBs primarily serve as subconscious tools for emotional regulation. OCD compulsions are conscious attempts to alleviate anxiety and prevent perceived harmful consequences. BFRB behaviors often occur with limited conscious awareness, while OCD sufferers are typically more aware of their compulsive actions.

Both conditions can significantly affect daily functioning and social interactions.BFRBs may lead to physical injuries and lowered self-esteem due to visible effects. OCD can cause severe anxiety and time-consuming rituals that interfere with daily activities.BFRB treatment emphasizes behavior modification and awareness techniques, while OCD treatment often involves exposure therapy to reduce anxiety responses.

Understanding these distinctions is crucial for accurate diagnosis and effective treatment. While both conditions present challenges, with proper support and intervention, individuals with BFRBs or OCD can learn to manage their symptoms and improve their overall quality of life.

The post BFRBs vs. OCD: Similarities and Differences appeared first on International OCD Foundation.

Verbal fluency after cochlear implantation: a longitudinal comparison with untreated hearing loss in the ELSA cohort

IntroductionHearing loss is associated with accelerated cognitive decline, and auditory rehabilitation via cochlear implantation (CI) may mitigate this trajectory. In the past, the impact of cochlear implantation on different cognitive subdomains has been described. However, verbal fluency (VF), which requires fast semantic retrieval, executive control, and processing speed, and is predictive of dementia risk and overall survival, has been rarely studied and control groups are mostly missing due to ethical reasons. The present study compares long-term VF trajectories in CI recipients and untreated hearing-impaired controls from a large population-based aging study.Materials and methodsVF was assessed in 74 CI recipients (M = 65.6 years, SD = 9.1) at pre-operative baseline and 1, 2, 4.5, and up to 9 years post-implantation, and in 383 untreated hearing-impaired participants (M = 72.6 years, SD = 10.0) from the English Longitudinal Study of Ageing (ELSA) across a comparable time frame. Scores were z-standardized within each study to enable cross-cohort comparison. Linear mixed-effects models were used to compare VF trajectories, with age, sex, and education as covariates.ResultsVF trajectories differed significantly between groups (Time × Study interaction: b = 0.562, p < 0.001). The ELSA cohort showed a steady linear decline over time (b = −0.261, p = 0.001), whereas the CI cohort exhibited an inverted-U trajectory with initial improvement followed by a plateau. After propensity score matching, results remained robust.ConclusionCochlear implantation is associated with more favorable long-term verbal fluency trajectories compared to untreated hearing loss. These findings add to the growing evidence that auditory rehabilitation may help preserve cognitive function in older adults.

High perceived energy: exploring distinct patterns of energetic and cognitive functioning in older adults

BackgroundA hyperthymic temperament and high perceived energy are often framed as positive dispositional traits, particularly in later life. Indeed, these characteristics are generally associated with a greater tendency to preserve autonomy and with more favorable outcomes in terms of health, engagement, and participation in community life-key dimensions of successful aging. However, the relationship between elevated perceived energy and cognitive performance in older adults remains insufficiently explored. Therefore, this exploratory study aimed to compare cognitive performance and well-being in older adults with different self-reported levels of subjective energy, and to explore potential phenotypic heterogeneity within this population.MethodsWe conducted a cross-sectional study on older adults from the general population. Participants were classified as having higher versus lower levels of perceived energy based on item 10 of the Short Form Health Survey (SF-12) questionnaire. Cognitive performance was assessed using the Addenbrooke’s Cognitive Examination-Revised (ACE-R). Depressive symptoms were evaluated with the Patient Health Questionnaire-9 (PHQ-9), biological and behavioral rhythm regulation with the Biological Rhythms Interview of Assessment in Neuropsychiatry (BRIAN), and quality of life with the SF-12 were also evaluated to characterize overall well-being.ResultsStatistically significant difference emerged in global cognitive performance between individuals with higher versus lower perceived energy (ES ACE-R p=0.012). Although subscale scores were within the normative range in both groups, individuals with higher perceived energy consistently showed significantly lower scores in language (p = 0.011) and attention/orientation (p = 0.049) compared to the lower perceived energy group. Interestingly, the higher perceived energy group also reported fewer depressive symptoms (p = 0.013), better social and behavioral rhythm regulation (p = 0.005), and higher quality of life (p < 0.001).Discussion/ConclusionsHyperenergy in older adults with preserved well-being is not associated with global cognitive impairment, but rather with subtle domain-specific performance variations, suggesting a heterogeneous underlying profile. These findings support a high-energy profile that does not appear to be associated with functional impairment, but is instead characterized by preserved or enhanced functioning and overall well-being, despite selective areas of lower performance. Longitudinal studies are needed to clarify its clinical and cognitive implications.

From promise to practice: artificial intelligence in mental health care in the MENA region

Mental health disorders represent a growing burden across the Middle East and North Africa (MENA) region, where depression and anxiety are highly prevalent amid conflict, displacement, and socioeconomic strain, affecting up to 40 percent of adults, yet treatment gaps remain at 80-95% due to provider shortages, financial strain, and cultural barriers. In this context, artificial intelligence (AI), in the form of large language models (LLMs) and specialized psychotherapy chatbots, may offer a scalable adjunct to help address these gaps through anonymous screening, predictive risk modeling, psychoeducation, and brief interventions. This narrative review examines current evidence of AI-driven conversational tools in mental health with a specific focus on their application, acceptance, and limitations within the MENA region. To do so, A structured search of MEDLINE and Embase (2000–2026) identified studies on conversational AI in mental health, prioritizing evidence from the MENA region and supplemented by relevant global literature. Overall, findings suggest that while these tools offer high accessibility and user engagement, particularly for low-intensity support, their effectiveness is limited by linguistic and cultural mismatches, including Arabic diglossia and poor alignment with locally grounded expressions of distress. At the same time, user acceptance reflects a paradox in which stigma and privacy concerns drive reliance on anonymous AI tools while simultaneously limiting trust in their clinical reliability, reinforcing a preference for hybrid models with human oversight. Taken together, these findings indicate that current systems remain insufficiently adapted to the MENA context, underscoring the need for culturally grounded, dialect-sensitive, and clinically supervised approaches to ensure safe and effective integration.

Beyond Theory of Mind: mentalization as a relational and developmental framework for autism

Autistic individuals and those around them often navigate social and emotional situations in which behaviors, intentions, and affects are difficult to interpret. Supporting mentalizing processes within child–caregiver interactions may help address these challenges; however, a broader conceptual shift is needed, moving beyond a narrow deficit-based perspective toward understanding mentalization as a multidimensional, relational, and developmental process. By shifting the focus from individual deficits to child–caregiver meaning-making processes, this framework may help clarify assessment and intervention targets and inform future research on psychopathological vulnerability in autism. This targeted narrative mini-review therefore aimed to summarize preliminary evidence suggests that other-related mentalizing may show greater difficulties than self-related mentalizing, although this hypothesis requires further replication. Findings also highlight caregiver mentalization, particularly parental reflective functioning, as a key relational process shaping how children’s behavior is interpreted, regulated, and responded to over time. In this light, preliminary intervention studies suggest that mentalization-based and mentalization-informed approaches may improve parental reflective functioning, cognitive reappraisal, self-efficacy, and inferential style, with potential indirect benefits for children’s emotional outcomes. We therefore propose a relational-developmental framework in which mentalization is conceptualized as a shared and dynamic process of meaning-making under conditions of social and emotional ambiguity. Adopting an individual, relational and developmentally informed perspective may contribute to the development of more precise assessment models, more targeted interventions, and a deeper understanding of mental health vulnerability in autism.

Construction and validation of multiple machine learning models for influencing factors of postpartum post-traumatic stress disorder in primiparas

ObjectiveTo analyze the multidimensional factors associated with postpartum post-traumatic stress disorder (PP-PTSD) in primiparas based on the Integrated Framework for Population Health Risk Management (IFPHRM), multiple machine learning-based predictive models were constructed and externally validated to identify high-risk individuals and to provide a robust evidence base for targeted preventive interventions.MethodsThis cross-sectional study consecutively enrolled 1, 135 primiparous women from the Department of Obstetrics at Hefei Maternal and Child Health Hospital between June 2024 and May 2025. Participants were divided chronologically into a training cohort and an independent temporal validation cohort. Women recruited from June 2024 to January 2025 were included in the training cohort (n = 794), whereas those recruited from February 2025 to May 2025 were included in the temporal validation cohort (n = 341). At six weeks postpartum, PP-PTSD symptoms were assessed using the Post-traumatic Stress Disorder Checklist-Civilian Version (PCL-C), with a score ≥38 indicating probable PP-PTSD. Multidimensional variables, including physiological and psychological factors, environmental and family-related factors, and social-behavioral factors, were collected. Candidate predictors were first screened using univariate analysis and then selected using least absolute shrinkage and selection operator (LASSO) regression. Multivariable logistic regression was used to identify independent associated factors. Seven machine learning models, including Logistic Regression, Naive Bayes, Support Vector Machine, Decision Tree, Gradient Boosting, AdaBoost, and Linear Discriminant Analysis, were constructed. Model performance was evaluated in the independent temporal validation cohort using receiver operating characteristic curves, calibration curves, decision curve analysis, and the DeLong test. SHAP analysis was used to interpret the optimal model.ResultsAmong the 794 participants in the training cohort, the incidence of PP-PTSD was 25.18%. Five key predictors were selected by LASSO regression: social support, depression, neonatal caregiving style, husband’s participation, and sleep quality. Multivariable logistic regression showed that depression and poor sleep quality were associated with an increased risk of PP-PTSD, whereas higher social support, greater husband’s participation, and parental assistance in neonatal care were associated with a reduced risk. Among the seven models, the Gradient Boosting model achieved the best overall performance in the temporal validation cohort, with an AUC of 0.939, F1 score of 0.700, specificity of 0.943, sensitivity of 0.651, and Youden index of 0.595. The DeLong test showed that Gradient Boosting performed significantly better than Logistic Regression. SHAP analysis further indicated that social support, husband’s participation, sleep quality, and depression were the major contributors to model prediction.ConclusionPostpartum PTSD (PP-PTSD) exhibits a higher incidence among primiparous women and exerts substantial adverse effects on maternal mental health, the mother–infant relationship, and overall family functioning. Guided by the Integrated Framework of Perinatal Health Risk Management (IFPHRM), this study elucidated the multidimensional mechanisms underlying PP-PTSD, encompassing physiological and psychological factors (e.g., sleep quality and depression), environmental and occupational factors (e.g., social support, paternal involvement, and infant caregiving practices), and social behavioral factors. The Gradient Boosting prediction model demonstrated robust performance and high predictive accuracy upon independent external validation, highlighting its potential utility for risk stratification and future clinical translation. Nevertheless, multicentre validation and the development of clinically implementable tools are warranted. Collectively, this study offers a theoretical foundation and methodological framework for the early identification, targeted intervention, and long-term health management of PP-PTSD in primiparous women.

Modeling Short-Term Symptom Changes and Behavioral Subtypes of Depression and Anxiety in the General Population: Observational Study Using Smartphone Data

Background: Smartphone-based digital phenotyping has emerged as a promising approach for monitoring mental health using passive behavioral data. Prior studies have linked smartphone-derived features to depression and anxiety severity; however, knowledge regarding whether short-term changes in symptoms can be captured using passive smartphone data in general population samples remains limited, as does the understanding of how such findings should be interpreted vis-à-vis behavioral patterns and demographic variability. Objective: This study aimed to model short-term changes in depression and anxiety severity using passive smartphone data, examine model performance across demographic subgroups, and identify behavioral patterns associated with symptom changes. Methods: We collected 2 weeks of smartphone usage data from 95 adults in the general population and assessed depressive and anxiety symptoms using the clinician-rated Hamilton Depression Rating Scale and Hamilton Anxiety Rating Scale, respectively. Behavioral features—including physical activity, app use, and screen usage metrics—were extracted and compressed using an autoencoder and principal component analysis. The resulting features—along with age, sex, and baseline Hamilton scores—were used to train random forest classifiers predicting symptom score changes (increase, decrease, or unchanged). Additionally, we examined whether model performance differed across demographic subgroups and whether models excluding baseline scores retained predictive performance, as baseline severity was expected to be a strong predictor. To add explanatory value beyond prediction, behavioral subtypes associated with symptom changes were identified by applying unsupervised clustering. Results: The model exhibited moderate performance in predicting changes in the Hamilton Depression Rating Scale (mean accuracy=0.70, mean area under the receiver operating characteristic curve=0.74) and Hamilton Anxiety Rating Scale (mean accuracy=0.65, mean area under the receiver operating characteristic curve=0.69) scores. Performance varied according to demographics, with reduced accuracy among younger adults and females, although these differences were not significant in permutation tests. Excluding baseline Hamilton scores diminished performance substantially, suggesting that baseline symptom severity accounted for a substantial proportion of the predictive performance. Clustering revealed 4 distinct behavioral subtypes according to smartphone usage patterns. A cluster characterized by structured, daytime-focused smartphone use and lower temporal entropy demonstrated greater improvement in depressive symptoms, whereas clusters with lower and irregular usage patterns exhibited minimal improvement or worsening. Conclusions: Passive smartphone-derived behavioral data demonstrated moderate ability to model short-term symptom changes in this predominantly nonclinical sample. However, a substantial proportion of the predictive performance was attributable to baseline symptom severity, underscoring that passive smartphone data may provide modest supplementary information rather than robust stand-alone predictive value. Nevertheless, clustering analyses indicated that passive data may still assist in identifying behaviorally distinct subtypes associated with different depressive symptom trajectories. These findings reflect a practical contribution to digital phenotyping research by elucidating both the potential and constraints of passive smartphone data for short-term symptom monitoring in small general population samples.
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Nonverbal AI-Based Communication Robot for Staff in Disaster-Affected Care Facilities: Exploratory ABAB Intervention Study

Background: Medical and welfare facilities in the Noto region of Japan were severely affected by the 2024 Noto Peninsula earthquake and subsequent torrential rains. Staff working in these facilities were disaster survivors and frontline caregivers with limited psychological support. Nonverbal social robots may provide companionship and emotional comfort; however, their effects on the health-related quality of life (QoL) and well-being of care staff in disaster-affected settings remain unclear. Objective: This study explored whether introducing a nonverbal artificial intelligence communication robot was associated with changes in health-related QoL and well-being among care facility staff working under disaster conditions. Secondary objectives were to evaluate safety, acceptability, and intention to continue use. Methods: This pragmatic, exploratory pilot study used an ABAB design conducted between February 2025 and June 2025. After a 2-week baseline period, staff in dementia care, general care, and short-stay units underwent 2-week intervention, withdrawal, reintervention, and withdrawal phases. Questionnaires were administered at each phase end. The primary outcomes were health-related QoL (EQ-5D-5L), well-being (World Health Organization–5 Well‑Being Index), and positive mental health (Mental Health Continuum–Short Form). Friedman tests compared outcomes across the 5 phases, and effect sizes were expressed as Kendall . Safety, acceptability, and intention to continue use were compared between the first and second intervention phases using Wilcoxon signed rank tests with Bonferroni adjustment and rank-biserial correlations as effect sizes. Results: Of the 58 staff who completed the baseline assessment, 49 (84.5%) were included in the analytic sample (25 in dementia care, 12 in general care, and 12 in short-stay units). Among these participants, 40 (81.6%) were women, and 38 (77.6%) reported disaster-related damage to their homes or families. In the pooled analysis, no phase effect was observed for the EQ-5D-5L (=.10; Kendall =0.032, negligible), the World Health Organization–5 Well‑Being Index (=.70; Kendall =0.016, negligible), or the Mental Health Continuum–Short Form (=.44; Kendall =0.022, negligible). No robot-related adverse events were reported. In the dementia care unit, nominal unadjusted differences were observed for “made me feel calm” (=.045; rank-biserial correlation =0.571, large), “like” (=.03; =0.559, large), and “felt at peace” (=.02; =0.718, large); however, none remained statistically significant after Bonferroni correction. Conclusions: The short-term use of a nonverbal artificial intelligence communication robot did not measurably improve health-related QoL or well-being among staff in disaster-affected care facilities. Deployment appeared feasible and was not associated with reported adverse events, but efficacy as a mental health support intervention remains unproven. Exploratory acceptability and interaction signals may inform future adequately powered studies.
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Active Ingredients in Digital Cognitive Interventions: Integrating Dismantling Designs With Mechanistic Neuroscience

Digital cognitive interventions (DCIs) have emerged as scalable approaches for treating cognitive dysfunction across psychiatric, neurological, and aging populations. Despite growing evidence of efficacy, little is known about which intervention components drive therapeutic effects or through which neurocognitive mechanisms they operate. As a result, null findings are often difficult to interpret, making it unclear whether interventions failed to engage their intended targets, or whether the targets themselves are not causally related to meaningful outcomes. This limits intervention refinement, comparative evaluation, and precision personalization. Here, we argue that DCI research should shift from broad efficacy testing toward mechanistic trials designed to identify active ingredients—the intervention components responsible for engaging prespecified neurocognitive targets and producing clinically meaningful benefits. We propose adapting dismantling design methodology from psychotherapy research in order to integrate Research Domain Criteria constructs, mechanistic neuroscience, and high-resolution digital behavioral data to identify factors driving cognitive and functional outcomes. This approach aligns with the National Institute of Mental Health experimental therapeutics framework by explicitly linking target specification and target engagement with downstream clinical and functional outcomes. Mechanistic dismantling trials can determine whether specific DCI features, including adaptive difficulty, reward schedules, feedback contingencies, task variability, cognitive targets, and human support, are necessary, sufficient, or synergistic for engaging neural circuitry and producing durable and clinically meaningful transfer. Beyond optimizing intervention design, such studies may transform null or negative trials into mechanistically interpretable findings, while clarifying disease mechanisms and supporting the development of personalized, optimized, and usable DCIs.
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